Citations

Purpose Accurate, individualized risk prediction for breast cancer is lacking. Tissue-based features may help to stratify women into different risk levels. Breast lobules are the anatomic sites of origin of breast cancer. As women age, these lobular structures should regress, which results in reduced breast cancer risk. However, this does not occur in all women. Methods We have quantified the extent of lobule regression on a benign breast biopsy in 85 patients who developed breast cancer and 142 age-matched controls from the Mayo Benign Breast Disease Cohort, by determining number of acini per lobule and lobular area. We also calculated Gail model 5-year predicted risks for these women. Results There is a step-wise increase in breast cancer risk with increasing numbers of acini per lobule (P = .0004). Adjusting for Gail model score, parity, histology, and family history did not attenuate this association. Lobular area was similarly associated with risk. The Gail model estimates were associated with risk of breast cancer (P = .03). We examined the individual accuracy of these measures using the concordance (c) statistic. The Gail model c statistic was 0.60 (95% CI, 0.50 to 0.70); the acinar count c statistic was 0.65 (95% CI, 0.54 to 0.75). Combining acinar count and lobular area, the c statistic was 0.68 (95% CI, 0.58 to 0.78). Adding the Gail model to these measures did not improve the c statistic. Conclusion Novel, tissue-based features that reflect the status of a woman's normal breast lobules are associated with breast cancer risk. These features may offer a novel strategy for risk prediction.

Purpose Recent changes have occurred in the presurgical planning for breast cancer, including the introduction of preoperative breast magnetic resonance imaging (MRI). We sought to analyze the trends in mastectomy rates and the relationship to preoperative MRI and surgical year at Mayo Clinic, Rochester, MN. Patients and Methods We identified 5,405 patients who underwent surgery between 1997 and 2006. Patients undergoing MRI were identified from a prospective database. Trends in mastectomy rate and the association of MRI with surgery type were analyzed. Multiple logistic regression was used to assess the effect of surgery year and MRI on surgery type, while adjusting for potential confounding variables. Results Mastectomy rates differed significantly across time (P < .0001), and decreased from 45% in 1997% to 31% in 2003, followed by increasing rates for 2004 to 2006. The use of MRI increased from 10% in 2003% to 23% in 2006 (P < .0001). Patients with MRI were more likely to undergo mastectomy than those without MRI (54% v 36%; P < .0001). However, mastectomy rates increased from 2004 to 2006 predominantly among patients without MRI (29% in 2003% to 41% in 2006; P < .0001). In a multivariable model, both MRI (odds ratio [OR], 1.7; P < .0001) and surgical year (compared to 2003 OR: 1.4 for 2004, 1.8 for 2005, and 1.7 for 2006; P < .0001) were independent predictors of mastectomy. Conclusion After a steady decline, mastectomy rates have increased in recent years with both surgery year and MRI as significant predictors for type of surgery. Further studies are needed to evaluate the role of MRI and other factors influencing surgical planning.

Purpose An accurate estimate of a woman's breast cancer risk is essential for optimal patient counseling and management. Women with biopsy-confirmed atypical hyperplasia of the breast (atypia) are at high risk for breast cancer. The Gail model is widely used in these women, but has not been validated in them. Patients and Methods Women with atypia were identified from the Mayo Benign Breast Disease (BBD) cohort (1967 to 1991). Their risk factors for breast cancer were obtained, and the Gail model was used to predict 5-year–and follow-up–specific risks for each woman. The predicted and observed numbers of breast cancers were compared, and the concordance between individual risk levels and outcomes was computed. Results Of the 9,376 women in the BBD cohort, 331 women had atypia (3.5%). At a mean follow-up of 13.7 years, 58 of 331 (17.5%) patients had developed invasive breast cancer, 1.66 times more than the 34.9 predicted by the Gail model (95% CI, 1.29 to 2.15; P < .001). For individual women, the concordance between predicted and observed outcomes was low, with a concordance statistic of 0.50 (95% CI, 0.44 to 0.55). Conclusion The Gail model significantly underestimates the risk of breast cancer in women with atypia. Its ability to discriminate women with atypia into those who did and did not develop breast cancer is limited. Health care professionals should be cautious when using the Gail model to counsel individual patients with atypia.

Learning Objectives After completing this course, the reader will be able to: Discuss the role of radiation therapy in the adjuvant treatment of DCIS.Describe the impact of tamoxifen in the treatment of DCIS.Discuss evolving strategies in neoadjuvant treatment for DCIS. Access and take the CME test online and receive 1 AMA PRA Category 1 Credit™ at CME.TheOncologist.com